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10.1080/00949655.2014.983111 http://scihub22266oqcxt.onion/10.1080/00949655.2014.983111 C4568435!4568435!26379316     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26379316.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Stat+Comput+Simul 2015 ; 85 (17): 3498-511 Nephropedia Template TP {{tp|p=26379316|t=2015. Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data |pdf=|usr=}}{{26379316}} gab.com Text Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data JO: J Stat Comput Simul http://www.kidney.de/pget.php?&tw=1&pmid=26379316 #FOAvip Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. Twit Text FOAVip Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data ????J Stat Comput Simul http://www.kidney.de/pget.php?&tw=1&pmid=26379316 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26379316 #FOAvip English Wikipedia <ref>{{Cite pmid|26379316}}</ref> Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data #MMPMID26379316 Karakaya J; Karabulut E; Yucel RM J Stat Comput Simul 2015[]; 85 (17): 3498-511 PMID26379316show ga Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. äSensitivity to imputation models and assumptions in receiver operating(epmc).pdf DeepDyve Pubget Overpricing